Next-generation DNA sequencing identifies novel gene variants and pathways involved in specific language impairment.

A significant proportion of children have unexplained problems acquiring proficient linguistic skills despite adequate intelligence and opportunity. Developmental language disorders are highly heritable with substantial societal impact. Molecular studies have begun to identify candidate loci, but much of the underlying genetic architecture remains undetermined. We performed whole-exome sequencing of 43 unrelated probands affected by severe specific language impairment, followed by independent validations with Sanger sequencing, and analyses of segregation patterns in parents and siblings, to shed new light on aetiology. By first focusing on a pre-defined set of known candidates from the literature, we identified potentially pathogenic variants in genes already implicated in diverse language-related syndromes, including ERC1, GRIN2A, and SRPX2. Complementary analyses suggested novel putative candidates carrying validated variants which were predicted to have functional effects, such as OXR1, SCN9A and KMT2D. We also searched for potential "multiple-hit" cases; one proband carried a rare AUTS2 variant in combination with a rare inherited haplotype affecting STARD9, while another carried a novel nonsynonymous variant in SEMA6D together with a rare stop-gain in SYNPR. On broadening scope to all rare and novel variants throughout the exomes, we identified biological themes that were enriched for such variants, including microtubule transport and cytoskeletal regulation.

A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication.

BACKGROUND: Bats are able to employ an astonishingly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vocal bat species, Phyllostomus discolor. RESULTS: Gene network analysis typically needs large sample sizes for correct clustering, this can be prohibitive where samples are limited, such as in this study. To overcome this, we developed a novel bioinformatics methodology for identifying robust co-expression gene networks using few samples (N=6). Using this approach, we identified tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalisation. The most highly connected network identified represented a cluster of genes involved in glutamatergic synaptic transmission. Glutamatergic receptors play a significant role in vocalisation from the PAG, suggesting that this gene network may be mechanistically important for vocal-motor control in mammals. CONCLUSION: We have developed an innovative approach to cluster co-expressing gene networks and show that it is highly effective in detecting robust functional gene networks with limited sample sizes. Moreover, this work represents the first gene network analysis performed in a bat brain and establishes bats as a novel, tractable model system for understanding the genetics of vocal mammalian communication.

Computational identification of new structured cis-regulatory elements in the 3'-untranslated region of human protein coding genes.

Messenger ribonucleic acids (RNAs) contain a large number of cis-regulatory RNA elements that function in many types of post-transcriptional regulation. These cis-regulatory elements are often characterized by conserved structures and/or sequences. Although some classes are well known, given the wide range of RNA-interacting proteins in eukaryotes, it is likely that many new classes of cis-regulatory elements are yet to be discovered. An approach to this is to use computational methods that have the advantage of analysing genomic data, particularly comparative data on a large scale. In this study, a set of structural discovery algorithms was applied followed by support vector machine (SVM) classification. We trained a new classification model (CisRNA-SVM) on a set of known structured cis-regulatory elements from 3'-untranslated regions (UTRs) and successfully distinguished these and groups of cis-regulatory elements not been strained on from control genomic and shuffled sequences. The new method outperformed previous methods in classification of cis-regulatory RNA elements. This model was then used to predict new elements from cross-species conserved regions of human 3'-UTRs. Clustering of these elements identified new classes of potential cis-regulatory elements. The model, training and testing sets and novel human predictions are available at: http://mRNA.otago.ac.nz/CisRNA-SVM.

Ancestral RNA: the RNA biology of the eukaryotic ancestor.

Our knowledge of RNA biology within eukaryotes has exploded over the last five years. Within new research we see that some features that were once thought to be part of multicellular life have now been identified in several protist lineages. Hence, it is timely to ask which features of eukaryote RNA biology are ancestral to all eukaryotes. We focus on RNA-based regulation and epigenetic mechanisms that use small regulatory ncRNAs and long ncRNAs, to highlight some of the many questions surrounding eukaryotic ncRNA evolution.

High throughput genome-wide survey of small RNAs from the parasitic protists Giardia intestinalis and Trichomonas vaginalis.

RNA interference (RNAi) is a set of mechanisms which regulate gene expression in eukaryotes. Key elements of RNAi are small sense and antisense RNAs from 19 to 26 nt generated from double-stranded RNAs. MicroRNAs (miRNAs) are a major type of RNAi-associated small RNAs and are found in most eukaryotes studied to date. To investigate whether small RNAs associated with RNAi appear to be present in all eukaryotic lineages, and therefore present in the ancestral eukaryote, we studied two deep-branching protozoan parasites, Giardia intestinalis and Trichomonas vaginalis. Little is known about endogenous small RNAs involved in RNAi of these organisms. Using Illumina Solexa sequencing and genome-wide analysis of small RNAs from these distantly related deep-branching eukaryotes, we identified 10 strong miRNA candidates from Giardia and 11 from Trichomonas. We also found evidence of Giardia short-interfering RNAs potentially involved in the expression of variant-specific surface proteins. In addition, eight new small nucleolar RNAs from Trichomonas are identified. Our results indicate that miRNAs are likely to be general in ancestral eukaryotes and therefore are likely to be a universal feature of eukaryotes.

Computational identification of four spliceosomal snRNAs from the deep-branching eukaryote Giardia intestinalis.

RNAs processing other RNAs is very general in eukaryotes, but is not clear to what extent it is ancestral to eukaryotes. Here we focus on pre-mRNA splicing, one of the most important RNA-processing mechanisms in eukaryotes. In most eukaryotes splicing is predominantly catalysed by the major spliceosome complex, which consists of five uridine-rich small nuclear RNAs (U-snRNAs) and over 200 proteins in humans. Three major spliceosomal introns have been found experimentally in Giardia; one Giardia U-snRNA (U5) and a number of spliceosomal proteins have also been identified. However, because of the low sequence similarity between the Giardia ncRNAs and those of other eukaryotes, the other U-snRNAs of Giardia had not been found. Using two computational methods, candidates for Giardia U1, U2, U4 and U6 snRNAs were identified in this study and shown by RT-PCR to be expressed. We found that identifying a U2 candidate helped identify U6 and U4 based on interactions between them. Secondary structural modelling of the Giardia U-snRNA candidates revealed typical features of eukaryotic U-snRNAs. We demonstrate a successful approach to combine computational and experimental methods to identify expected ncRNAs in a highly divergent protist genome. Our findings reinforce the conclusion that spliceosomal small-nuclear RNAs existed in the last common ancestor of eukaryotes.

Combined experimental and computational approach to identify non-protein-coding RNAs in the deep-branching eukaryote Giardia intestinalis.

Non-protein-coding RNAs represent a large proportion of transcribed sequences in eukaryotes. These RNAs often function in large RNA-protein complexes, which are catalysts in various RNA-processing pathways. As RNA processing has become an increasingly important area of research, numerous non-messenger RNAs have been uncovered in all the model eukaryotic organisms. However, knowledge on RNA processing in deep-branching eukaryotes is still limited. This study focuses on the identification of non-protein-coding RNAs from the diplomonad parasite Giardia intestinalis, showing that a combined experimental and computational search strategy is a fast method of screening reduced or compact genomes. The analysis of our Giardia cDNA library has uncovered 31 novel candidates, including C/D-box and H/ACA box snoRNAs, as well as an unusual transcript of RNase P, and double-stranded RNAs. Subsequent computational analysis has revealed additional putative C/D-box snoRNAs. Our results will lead towards a future understanding of RNA metabolism in the deep-branching eukaryote Giardia, as more ncRNAs are characterized.